KIDNEY STONE DETECTION

USING IMAGE PROCESSING

Submitted By:

Mayank (05915603119)

Madhusudan Kaushik (07515603117)

Tushar Mawa (20215603119)

Akshat Jain (21015603119)

Contents

•What is image processing?

•Types of image processing

•Kidney stone detection: Need of the hour

•Role of image processing in kidney stone detection

•Use Case: Virtual Path Labs

•Code Flow

•Use Case Diagrams

•Results Glimpse

•References

What Is Image Processing?

•Image processing is the process of transforming an image into a

digital form and performing certain operations to get some useful

information from it. The image processing system usually treats all

images as 2D signals when applying certain predetermined signal

processing methods.

Types Of Image Processing

•There are five main types of image processing:

•Visualization - Find objects that are not visible in the image.

•Recognition - Distinguish or detect objects in the image.

•Sharpening and restoration - Create an enhanced image from the

original image.

•Pattern recognition - Measure the various patterns around the

objects in the image.

•Retrieval - Browse and search images from a large database of

digital images that are similar to the original image.

Kidney Stone Detection: Need Of The Hour

•Kidney stone disease is one of the risks for life throughout the

world and majority of people with stone formation in kidney at

the initial stage do not notice it as disease and it damages the

organ slowly. Current estimation is that there are 30 million

people suffering by this disease. There are different imaging

techniques for diagnosing kidney diseases, such as CT images, X-

rays, and Ultrasound imaging.

Role Of Image Processing In Kidney Stone

Detection

•Medical imaging plays a significant role in different clinical

applications such as medical procedures used for early detection,

monitoring, diagnosis, and treatment evaluation of various

medical conditions.

•In this project we are using one of the most important technique

of image processing called recognition. Image recognition is the

process of identifying an object or a feature in an image or video.

It is used in many applications like defect detection, medical

imaging, and security surveillance.

USER INTERFACE

Use Case: Virtual Path Labs

•What is Virtual Path Labs?

•It is a website that any user can use to detect stones in kidneys

using ultrasound or CT images.

•Simply the user has to upload a computer generated ultrasound or

CT image and it will show the detection results.

•It integrates MATLAB at its backend which is used to detect kidney

stones.

•By incorporating this technology in a website we ensure that it

becomes economical as the imaging centres will not require extra

equipment's to accomplish this.

Code Flow

•For accomplishing this project we are going to code in MATLAB using

MATLAB programming language.

•Firstly we will input the image we want to process by concatenating the

pathname and filename and using the imshow() function to display the

image.

•Then we will convert the image from rgb to gray scale using rgb2gray()

function and will be extracting the pixel information like at a coordinate

how many pixels are present and will use it to binarize the image using

binarize() function.

•Next we will filter the image and display only the pixels above a certain

threshold and filter all the unnecessary details in the image using

bwareopen() function.

•Next we will further pre-process the image by multiplying a

binary mask on an rgb image followed by contrast stretching.

•Next we will define the dimensions mostly between 0.3 and 0.7

using imadjust() function.

•Next we do the median filtering for the greyscale image using

the midfilt2() function. The following operations converges us

further to the desired result like gradient descent in linear

regression.

•And finally using the region of interest polynomial(roipoly)

create a mask/filter on the central(concerned) and check

whether binary object(stones) is present there or not.

USE CASE DIAGRAMS

RESULTS GLIMPSE

Input Image Output Image

Console Output

Input Image Output Image

Console Output

References

•https://www.hindawi.com/journals/vlsi/2015/581961/

•https://iopscience.iop.org/article/10.1088/2631-

8695/ac8b65/meta

•http://cse.anits.edu.in/projects/projects1920A4.pdf

•https://www.simplilearn.com/image-processing-article

•https://www.mathworks.com/discovery/image-recognition-

matlab.html